Search Results (244)

To mitigate low-speed speed oscillations in permanent magnet synchronous motors (PMSMs) arising from the combined effects of rotor-position-related periodic disturbances and external perturbations, this paper develops a composite robust speed regulation scheme that integrates non-singular terminal sliding mode control (NTSMC) with angle-domain iterative learning control (ILC). First, a non-singular terminal sliding mode speed controller is established to remove the singularity inherent in conventional terminal sliding mode formulations while preserving finite-time error convergence. To further improve robustness and reduce chattering, an enhanced generalized super-twisting reaching law incorporating a continuous saturation function is introduced. Second, to compensate for periodic disturbances associated with rotor position, an angle-domain ILC law is constructed to iteratively learn the periodic speed-tracking error, thereby suppressing low-speed speed ripple. Meanwhile, an extended state observer (ESO) is incorporated to estimate aperiodic disturbances online, enabling coordinated rejection of disturbances with different temporal characteristics. Experimental results demonstrate that the proposed composite strategy effectively weakens the dominant harmonic components in speed fluctuation and enhances low-speed operational smoothness, confirming the effectiveness of the developed method. Full article

To solve the problems of rotor position estimation error caused by the installation deviation of Hall sensors and the increase in yarn amount detection error in complex environments, resulting in speed fluctuations and unstable yarn feeding in the traditional permanent magnet synchronous motor (PMSM) drive system for yarn feeder, a control method for yarn amount in yarn feeder PMSMs based on an improved dual second-order generalized integrator (DSOGI) and Kalman filter is proposed. Firstly, in order to reduce the influence of installation deviation of Hall sensors, the three-phase Hall signals are converted into two-phase orthogonal Hall vector signals. An improved DSOGI is used to filter out high-order harmonic components and specific harmonic components in the Hall vector signals, and a cross-coupled structure is constructed to further enhance the fundamental component and suppress high-order harmonic components of negative coefficients. Then, accurate motor rotor position information is extracted by a quadrature phase-locked loop; secondly, in order to obtain accurate information on yarn amount, a system state model based on yarn amount and its rate of change is established, and Kalman filtering is used for optimal estimation of the yarn amount; finally, the above methods are integrated into the PMSM control system of the yarn feeder. Experimental results show that, compared with traditional methods, the PMSM control system of the yarn feeder using the method proposed in this paper has a shorter startup time and smaller steady-state error in motor speed and yarn amount when conveying yarn at a constant speed; when transporting yarn at variable speed, the motor speed and yarn amount settling time are shorter, and the peak deviation is smaller. Full article

The dynamic performance of sensorless PMSM control systems at low switching frequency is suboptimal due to issues such as position estimation error with proportional-intregral (PI)-based phase-locked loops (PLLs) and the inaccuracy of the system model. To solve this problem, this article first proposes an improved PLL based on a deadbeat controller with a damping factor (DDCPLL). It rapidly converges the estimated rotor position to the actual position during speed transients while ensuring the smoothness of the estimated speed. This approach reduces the number of undetermined coefficients from two in traditional methods to one, simplifying the structure and facilitating implementation. Additionally, to address the current cross-coupling issues in traditional current control strategies at low switching frequencies, a novel controller was directly designed based on the discrete-domain model of the PMSM. Then, by integrating the proposed DDCPLL, a discrete-domain model of the entire positionless control system was established. The parameters of the speed controller are optimized to further enhance the system’s dynamic response performance. Furthermore, the robustness of the system under speed estimation errors and motor parameter variations is analyzed. The results demonstrate that the proposed control system maintains good robustness despite the presence of speed estimation errors and parameter mismatches. The effectiveness and feasibility of the proposed PLL and control strategy are demonstrated by detailed results. Full article

High-performance operation of permanent magnet synchronous motors (PMSMs) strongly depends on the reliable availability of rotor position and speed information. Although this information is commonly obtained using physical position sensors, such sensors increase system cost and structural complexity and may reduce long-term reliability, particularly in demanding operating environments. In this study, a model-based, discrete-time, nonlinear gradient observer is adapted for the sensorless estimation of rotor speed and position in PMSMs. The developed Runge–Kutta model-based gradient observer (RKGO) utilizes stator voltage inputs and measured stator currents within a mathematical motor model to estimate the system states. In contrast to conventional sensorless estimation approaches, the adopted observer framework exploits discretization-based gradient dynamics to enhance numerical robustness and convergence behavior under nonlinear operating conditions. The observer design specifically targets stable and accurate state estimation in discrete-time implementations, with a particular focus on low-speed operating conditions. The performance of the adapted method is experimentally evaluated under low-speed operating conditions, including transient and steady-state operation. Real-time implementation is carried out on a dSPACE DS1104 control platform, including loaded acceleration scenarios to assess practical robustness. In addition, a comparative analysis with the Extended Kalman Filter (EKF) and the Runge–Kutta Extended Kalman Filter (RKEKF) is conducted at 60 rad/s under identical experimental conditions. Experimental results show that the RKGO method achieves accurate steady-state speed and position estimation with acceptable transient performance. The findings demonstrate that RKGO can be considered a viable alternative for low-speed sensorless PMSM drive applications. Full article

Advances in semiconductor technologies, particularly in power transistors and switching diodes, have enabled higher switching frequencies and converter efficiency, renewing interest in Switched Reluctance Motors (SRMs) for electric vehicles. This work presents a data-driven approach utilizing a Long Short-Term Memory (LSTM) network capable of effectively managing temporal dependencies for estimating rotor position without sensors in SRMs. The motor investigated was custom-designed, subsequently manufactured as a prototype. The LSTM was trained and validated with experimental data collected at various speeds and load conditions. The outcomes demonstrate the model’s strong performance, with a mean squared error (MSE) of ${1.77}^{{°}^2}$, a mean absolute error (MAE) of 1.09°, and 97.35% accuracy. Compared to typical estimation methods such as back-electromotive force (EMF)-based techniques, fuzzy logic, model predictive control, feed-forward neural networks (FFNNs), and back-propagation neural networks (BPNNs), the LSTM stands out as one of the most effective and widely used models. Previous neural networks (NN)-based studies typically report ±5° accuracy, whereas LSTM keeps the error about 1° in this study. This strategy eliminates position sensors, reduces cost and complexity, and enables reliable real-time SRM control. Results indicate that the method has significant potential for electric motor drives, particularly for SRMs. Full article

To reduce vehicular emission pollution in cold regions and maximize sustainable development of transportation, AC self-heating of electric vehicles is acknowledged as an efficient approach to mitigate the decline in Li-ion battery performance under low-temperature conditions. This paper introduces a novel battery self-heating approach based on reconfiguration of the drive circuit, which is compatible with both preheating and on-route heating. The undesired torque generated by the heating current can be inherently nullified regardless of the rotor position. The control of heating and driving currents is entirely decoupled, facilitating straightforward adaptation to a range of heating strategies. Furthermore, a battery electro-thermal model is proposed and integrated with the drive system model to estimate the battery temperature evolution. Comprehensive experiments are designed to validate the operating principle and the accuracy of battery temperature estimation under various working conditions. The results present a high fidelity between the experimental data and the simulation outcomes. The root mean square errors of the predicted battery temperature under all the constant and combined driving conditions are less than 1 °C. Full article

This paper proposes a sensorless disturbance-rejection control method for the threaded tightening process, aiming to eliminate the dependence on position sensors, thereby reducing system costs and mitigating the risks associated with sensor failures. The method involves the design of a speed loop control circuit with superior disturbance-rejection performance under typical tightening conditions, a smooth sensorless switching strategy, and an optimization of the torque-angle-based tightening process specifically addressing the challenge of rotor position estimation at low speeds. Furthermore, an integrated system for process fault monitoring and type feedback is incorporated. Simulation and experimental results demonstrate that the proposed system achieves improved speed loop tracking accuracy. The transition from I/F control to sliding mode control is smooth, accompanied by a significant reduction in speed distortion and a response time acceleration of 0.02 s. Within the rated tightening range, the tightening accuracy of the proposed system is only about 1% lower than that of a traditional PI system with position sensors, while it saves 20–30% of the total system cost attributable to position sensors and effectively avoids sensor failure risks, resulting in substantial overall advantages. The system presented in this study offers a technical path characterized by low cost and high reliability for high-precision assembly operations, with potential applications in high-end equipment fields such as aerospace, new energy vehicles, and semiconductor manufacturing. Its precise speed control lays the foundation for more refined torque-angle monitoring and full-process traceability of assembly quality. Full article

To address the challenges of diminished back-EMF, high noise interference, and reduced accuracy in traditional low-speed sensorless control, this study proposes a rotor position estimation method based on structural vibration characteristics. The coupling mechanism between air-gap electromagnetic force density and stator structural vibration modes is analyzed. This analysis reveals that rotor spatial information is embedded within specific modal phases, establishing the physical basis for utilizing vibration phase as a position carrier. Accordingly, a workflow encompassing signal acquisition, modal selection, and phase calculation is developed and integrated into a sensorless control system. Simulation results demonstrate that the proposed method achieves stable estimation even under strong noise. The estimation error shows clear performance advantages over conventional back-EMF-based methods in the low-speed region, validating its effectiveness and robustness at low speeds. This research provides a new approach that introduces non-electrical structural information as a complementary channel to overcome the inherent limitations of electrical-signal-based position estimation at low speeds. Full article

To address the chattering phenomenon and sensitivity to load disturbances in conventional sliding mode observers (SMO) for sensorless permanent magnet synchronous motor (PMSM) control, this paper proposes a robust sensorless control strategy integrating a fuzzy adaptive SMO with an improved sliding mode speed controller. In the observer design, a continuous hyperbolic tangent function, $tanh \left(ax\right)$, replaces the traditional sign function, while a fuzzy logic controller adaptively tunes the convergence factor a to enhance estimation accuracy and suppress high-frequency chattering. Simultaneously, an adaptive quadrature phase-locked loop (AQPLL) is incorporated to achieve adaptive matching across various operating conditions by updating parameters online, which effectively reduces phase delay and improves the dynamic performance of rotor position and speed estimation. Furthermore, a non-singular terminal sliding mode control (NTSMC) strategy is employed in the outer speed loop with a proposed segmented terminal reaching law. This law ensures rapid response in large-error regions and mitigates steady-state oscillations in small-error regions, thereby strengthening system robustness against load disturbances. The stability of the proposed system is rigorously verified via Lyapunov stability analysis. Simulation and experimental results demonstrate that the proposed approach significantly reduces speed and position estimation errors under varying speeds and sudden load changes compared to the conventional SMO-PI method, while effectively suppressing system chattering to confirm its engineering feasibility. Full article

In this paper, a sensorless field-oriented vector control (FOC) strategy combining an improved adaptive sliding mode observer (IASMO) and a finite-position-set phase-locked loop (FPS-PLL) is proposed for a surface permanent magnet synchronous motor (SPMSM) operating in the medium- and high-speed range. Firstly, a sliding mode observer (SMO) that can realize the observation of back electromotive force (back-EMF) is proposed, and an adaptive reaching law that can reduce the sliding mode coefficient is designed to help the SMO observe the back-EMF for the purpose of reducing chattering as well as verifying the stability of the system. Then, the FPS-PLL is used instead of a phase-locked loop (PLL) to extract the rotor position information from the observed back-EMF, thus avoiding the time-consuming process of tuning the PI parameters. The proposed FPS-PLL reduces the number of iterations from 64 to 20 while maintaining effective estimation performance. Finally, the effectiveness of the proposed scheme in suppressing chattering and maintaining comparable estimation accuracy while reducing computational burden is demonstrated by experiments. Full article

This study introduces a sensorless control approach for permanent magnet synchronous motors (PMSMs) that employs an Improved Non-Singular Fast Terminal Sliding Mode Controller (IMNFTSMC) and an Improved Non-Singular Fast Terminal Sliding Mode Observer (IMNFTSMO). The IMNFTSMC employs a novel hybrid reaching law and a continuous piecewise square root switching function to achieve faster convergence and effective chattering reduction over the conventional Sliding Mode Controller (SMC). This design successfully replaces two critical components: the discontinuous constant velocity term (a key component of the traditional SMC reaching law that is a primary source of control chattering in PMSM torque regulation) and the high-gain exponential term (which tends to induce overshoot during transient speed adjustments and degrade steady-state control precision). In the IMNFTSMO, a hybrid approach combining linear and non-singular terminal sliding modes eliminates phase lag associated with low-pass filtering in traditional sliding mode observers, improving rotor position and speed estimation accuracy. Stability of both IMNFTSMC and IMNFTSMO is rigorously proven using Lyapunov stability theory.Validation through extensive simulations and hardware experiments, including challenging zero-speed start, speed stepping, and load disturbance tests, confirms the proposed strategy provides improved dynamic response, effective anti-disturbance capability, and high accuracy for rotor position and speed estimation compared to established benchmark methods, demonstrating its feasibility for mid-to-low speed sensorless PMSM drives. Full article

Magnetic bearing hydrogen recirculation pumps enable high-speed, wear-free operation but are sensitive to flow-induced disturbances. This study proposes a cascade-structured disturbance observer (CDOB) that fuses rotor displacement and bearing current signals to achieve robust disturbance estimation under model uncertainties, with low-pass filtering to ensure stability. The proposed method is validated through simulations and repeated experiments at multiple axial positions. Based on raw displacement signals, the CDOB reduces the average peak-to-peak axial displacement by approximately 35% compared with a traditional disturbance observer. Under axial offset conditions of −100 μm and +100 μm, the CDOB consistently achieves average displacement reductions of approximately 23% and 24%, respectively, demonstrating consistent disturbance suppression and robustness across repeated tests. These results indicate that the CDOB provides an effective and practical solution for disturbance suppression in magnetic bearing systems. Full article

In electric vehicle and marine propulsion applications, the stable operation of permanent-magnet synchronous motor (PMSM) drive systems relies on accurate rotor position information. Such information is typically obtained from position sensors, which are prone to high temperature, humidity, vibration, and electromagnetic interference, leading to elevated failure rates; moreover, sensor installation introduces additional interfaces and wiring, thereby reducing system reliability. To address these issues, this paper proposes a sensorless control method based on an adaptive full-order sliding-mode observer (SMO). The proposed method employs the SMO output as the observer feedback correction term rather than the estimated back EMF, thereby avoiding substantial high-frequency noise. Furthermore, an S-shaped nonlinear function is designed to replace the conventional switching function, mitigating high-frequency chattering when the system operates in sliding mode; an adaptive sliding-mode gain function is designed, the sliding-mode gain and the boundary-layer thickness are adaptively tuned as a function of motor speed, which effectively enhances the back EMF estimation accuracy over a wide operating-speed range. The effectiveness of the proposed method is validated on a 2.3-kW PMSM experimental platform. Full article

To address the performance degradation of the traditional Extended Kalman Filter (EKF) in state estimation for sensorless brushless DC motor (BLDC) control under dynamic operating conditions, such as sudden speed and load changes—a degradation caused primarily by model mismatches—this paper proposes an Adaptive Extended Kalman Filter (AEKF) algorithm. The proposed algorithm incorporates a robust weighting strategy based on the Mahalanobis distance and a dynamically adjusted adaptive forgetting factor. This integration establishes an estimation mechanism capable of online updating of the innovation covariance, thereby enhancing the state observer’s adaptability to system uncertainties and external disturbances. Simulation results demonstrate that, compared to the traditional EKF, the designed AEKF algorithm significantly improves the estimation accuracy of rotor position and speed under various operating conditions, including low-speed start-up, speed step changes, and sudden load applications. Furthermore, it accelerates dynamic response, suppresses overshoot, and enhances the system’s disturbance rejection robustness. This work provides an effective state estimation solution for high-dynamic performance sensorless control of BLDC. Full article

Permanent magnet synchronous motors (PMSMs) are widely preferred in modern applications due to their high efficiency, high torque-to-inertia ratio, high power factor, and rapid dynamic response. Achieving optimal PMSM performance requires precise control, which depends on accurate estimation of motor speed and rotor position. This information is traditionally obtained through sensors such as encoders; however, these devices increase system cost and introduce size and integration constraints, limiting their use in many PMSM-based applications. To overcome these limitations, sensorless control strategies have gained significant attention. Since PMSMs inherently exhibit nonlinear dynamic behavior, accurate modeling of these nonlinearities is essential for reliable sensorless operation. In this study, a Runge–Kutta Extended Kalman Filter (RKEKF) approach is developed and implemented to enhance estimation accuracy for both rotor position and speed. The developed method utilizes the applied stator voltages and measured phase currents to estimate the motor states. Experimental validation was conducted on the dSPACE DS1104 platform under various operating conditions, including forward and reverse rotation, acceleration, low- and high-speed operation, and loaded operation. Furthermore, the performance of the developed RKEKF under load was compared with the conventional Extended Kalman Filter (EKF), demonstrating its improved estimation capability. The real-time feasibility of the developed RKEKF was experimentally verified through execution-time measurements on the dSPACE DS1104 platform, where the conventional EKF and the RKEKF required 47 µs and 55 µs, respectively, confirming that the proposed approach remains suitable for real-time PMSM control while accommodating the additional computational effort associated with Runge–Kutta integration. Full article